testLmAdjustedRSquare      package:rotRPackage      R Documentation

_A_d_j_u_s_t_e_d $_R^_2$ _t_e_s_t _f_o_r _a _l_i_n_e_a_r _m_o_d_e_l.

_D_e_s_c_r_i_p_t_i_o_n:

     This ROT function, called from a Test C++ object, is given two
     samples, a   scalar, a parameter vector and the required test
     level. It predicts the  values corresponding to the explanatory
     variables through the linear model,  then computes the Adjusted
     $R^2$. It is tested against the scalar, then the  function returns
     the result of the test and the Adjusted $R^2$ value.

_U_s_a_g_e:

     testLmAdjustedRSquare(x, beta, y, testLevel = 0.95)

_A_r_g_u_m_e_n_t_s:

       x: A m-by-n matrix containing the explanatory variables.

    beta: A n-by-1 vector containng the linear model parameters.

       y: A n-by-1 vector containng the response variables.

testLevel: the test level. (scalar in [0:1])

_D_e_t_a_i_l_s:

     As it is not asked in LinearModel.getPredict(), no prediction
     interval  is returned; it is up to the user to be careful about
     that. It is also to   noted that the sample is not assumed to
     contain the '1's corresponding to   the intercept parameter.

_V_a_l_u_e:

     A list is returned, containing two scalars ,                   

testResult: A scalar simulating a boolean (easier for Rserve)

valueAdjustedRSquare: A scalar.

_A_u_t_h_o_r(_s):

     Pierre-Matthieu Pair, Softia for EDF.

_E_x_a_m_p_l_e_s:

     set.seed(1)
     x <- matrix(runif(40), 10, 4)
     r <- matrix(c(1,2,3,4), 4, 1)
     y <- x %*% r + matrix(rnorm(10, 0, 0.05), 10, 1)
     LM <- computeLinearModel(x, y)
     testLmAdjustedRSquare(x, LM$parameterEstimate, y) 

